As companies are becoming increasingly dependent on Artificial Intelligence (AI) for efficiency, automation, and customization, learning AI has become pivotal. However, not everyone is an expert in the domain. Salesforce offers a series of short AI-training courses on its Trailhead platform to equip individuals with essential AI skills, promoting them towards new opportunities and career advancement. These courses are designed to help you become the AI professional your organization needs and are detailed as follows:
The ‘Artificial Intelligence Fundamentals’ course covers the basics of AI, including key concepts, challenges in defining AI, and functions it can perform. It explains the differences between hand-coded algorithms and trained models, the connection between machine learning and AI, and how data types affect training.
The ‘Large Language Models’ course focuses on large language models (LLMs), their training, and fine-tuning. The program guides you in selecting a suitable LLM for your organization and understanding the limitations of different LLM options.
The ‘Generative AI Basics’ course delves into generative AI, its capabilities, and the underlying technology. It addresses commonly raised business queries about generative AI and walks you through key vocabulary, tech stack, and rapid development components.
The ‘Natural Language Processing Basics’ course discusses natural language processing (NLP)’s fundamentals, the field’s evolution, and its daily applications. It differentiates between NLP, natural language understanding, and generation and explains techniques such as sentiment, intent, and context analysis.
The ‘Einstein Classification Apps for Service’ course offers training on using Einstein classification apps to supplement case data with predictive intelligence, thereby saving time for support agents. This course explains how to prepare data, implement apps, adjust prediction settings, and equip agents to use Einstein’s suggestions.
The ‘Prompt Fundamentals’ course guides you on designing effective prompts for generative AI. It explains prompt design, creating prompt templates, adding CRM data to prompts, and refining the design procedure.
The ‘Model Fine-Tuning’ course deals with the advantages, mechanics, and challenges of fine-tuning large language models. It provides clear guidance on when to use fine-tuning and ways to organize datasets. An overview of the fine-tuning process is also provided.
The ‘Generative AI for Images’ course presents techniques for creating high-quality images with AI, putting emphasis on the advantages of diffusion models over GANs. It discusses generative AI tools, setting achievable goals, designing efficient prompts, and addressing concerns about AI-generated artwork’s ethical implications.
The ‘Responsible Creation of Artificial Intelligence’ course informs about developing ethical AI systems by eliminating bias from data and algorithms. It teaches how to define bias and fairness, form diverse teams, align design with company values, comprehend ethical versus legal standards, and enforce best practices to identify and mitigate ethical risks in AI development.
Lastly, the ‘Critical Thinking and Decision-Making with Data and AI’ course equips participants with strategies for decision-making using data and AI and includes critical thinking, data-driven decision-making, and integrating AI. It offers instruction on methods of questioning, decision-making models, real-world applications, and ethical implications across various sectors.
By participating in these courses, you can gain a competitive edge in your career and position yourself as a sought-after AI professional in your organization.
